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What is quantum advantage? A quantum computing scientist explains an approaching milestone marking the arrival of extremely powerful computers

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What is quantum advantage? A quantum computing scientist explains an approaching milestone marking the arrival of extremely powerful computers

IBM’s quantum computer got President Joe Biden’s attention.
Mandel Ngan/AFP via Getty Images

Daniel Lidar, University of Southern California

Quantum advantage is the milestone the field of quantum computing is fervently working toward, where a quantum computer can solve problems that are beyond the reach of the most powerful non-quantum, or classical, computers.

Quantum refers to the scale of atoms and molecules where the laws of physics as we experience them break down and a different, counterintuitive set of laws apply. Quantum computers take advantage of these strange behaviors to solve problems.

There are some types of problems that are impractical for classical computers to solve, such as cracking state-of-the-art encryption algorithms. Research in recent decades has shown that quantum computers have the potential to solve some of these problems. If a quantum computer can be built that actually does solve one of these problems, it will have demonstrated quantum advantage.

I am a physicist who studies quantum information processing and the control of quantum systems. I believe that this frontier of scientific and technological innovation not only promises groundbreaking advances in computation but also represents a broader surge in quantum technology, including significant advancements in quantum cryptography and quantum sensing.

The source of quantum computing’s power

Central to quantum computing is the quantum bit, or qubit. Unlike classical bits, which can only be in states of 0 or 1, a qubit can be in any state that is some combination of 0 and 1. This state of neither just 1 or just 0 is known as a quantum superposition. With every additional qubit, the number of states that can be represented by the qubits doubles.

This property is often mistaken for the source of the power of quantum computing. Instead, it comes down to an intricate interplay of superposition, interference and entanglement.

Interference involves manipulating qubits so that their states combine constructively during computations to amplify correct solutions and destructively to suppress the wrong answers. Constructive interference is what happens when the peaks of two waves – like sound waves or ocean waves – combine to create a higher peak. Destructive interference is what happens when a wave peak and a wave trough combine and cancel each other out. Quantum algorithms, which are few and difficult to devise, set up a sequence of interference patterns that yield the correct answer to a problem.

Entanglement establishes a uniquely quantum correlation between qubits: The state of one cannot be described independently of the others, no matter how far apart the qubits are. This is what Albert Einstein famously dismissed as “spooky action at a distance.” Entanglement’s collective behavior, orchestrated through a quantum computer, enables computational speed-ups that are beyond the reach of classical computers.

The ones and zeros – and everything in between – of quantum computing.

Applications of quantum computing

Quantum computing has a range of potential uses where it can outperform classical computers. In cryptography, quantum computers pose both an opportunity and a challenge. Most famously, they have the potential to decipher current encryption algorithms, such as the widely used RSA scheme.

One consequence of this is that today’s encryption protocols need to be reengineered to be resistant to future quantum attacks. This recognition has led to the burgeoning field of post-quantum cryptography. After a long process, the National Institute of Standards and Technology recently selected four quantum-resistant algorithms and has begun the process of readying them so that organizations around the world can use them in their encryption technology.

In addition, quantum computing can dramatically speed up quantum simulation: the ability to predict the outcome of experiments operating in the quantum realm. Famed physicist Richard Feynman envisioned this possibility more than 40 years ago. Quantum simulation offers the potential for considerable advancements in chemistry and materials science, aiding in areas such as the intricate modeling of molecular structures for drug discovery and enabling the discovery or creation of materials with novel properties.

Another use of quantum information technology is quantum sensing: detecting and measuring physical properties like electromagnetic energy, gravity, pressure and temperature with greater sensitivity and precision than non-quantum instruments. Quantum sensing has myriad applications in fields such as environmental monitoring, geological exploration, medical imaging and surveillance.

Initiatives such as the development of a quantum internet that interconnects quantum computers are crucial steps toward bridging the quantum and classical computing worlds. This network could be secured using quantum cryptographic protocols such as quantum key distribution, which enables ultra-secure communication channels that are protected against computational attacks – including those using quantum computers.

Despite a growing application suite for quantum computing, developing new algorithms that make full use of the quantum advantage – in particular in machine learning – remains a critical area of ongoing research.

a metal apparatus with green laser light in the background
A prototype quantum sensor developed by MIT researchers can detect any frequency of electromagnetic waves.
Guoqing Wang, CC BY-NC-ND

Staying coherent and overcoming errors

The quantum computing field faces significant hurdles in hardware and software development. Quantum computers are highly sensitive to any unintentional interactions with their environments. This leads to the phenomenon of decoherence, where qubits rapidly degrade to the 0 or 1 states of classical bits.

Building large-scale quantum computing systems capable of delivering on the promise of quantum speed-ups requires overcoming decoherence. The key is developing effective methods of suppressing and correcting quantum errors, an area my own research is focused on.

In navigating these challenges, numerous quantum hardware and software startups have emerged alongside well-established technology industry players like Google and IBM. This industry interest, combined with significant investment from governments worldwide, underscores a collective recognition of quantum technology’s transformative potential. These initiatives foster a rich ecosystem where academia and industry collaborate, accelerating progress in the field.

Quantum advantage coming into view

Quantum computing may one day be as disruptive as the arrival of generative AI. Currently, the development of quantum computing technology is at a crucial juncture. On the one hand, the field has already shown early signs of having achieved a narrowly specialized quantum advantage. Researchers at Google and later a team of researchers in China demonstrated quantum advantage for generating a list of random numbers with certain properties. My research team demonstrated a quantum speed-up for a random number guessing game.

On the other hand, there is a tangible risk of entering a “quantum winter,” a period of reduced investment if practical results fail to materialize in the near term.

While the technology industry is working to deliver quantum advantage in products and services in the near term, academic research remains focused on investigating the fundamental principles underpinning this new science and technology. This ongoing basic research, fueled by enthusiastic cadres of new and bright students of the type I encounter almost every day, ensures that the field will continue to progress.The Conversation

Daniel Lidar, Professor of Electrical Engineering, Chemistry, and Physics & Astronomy, University of Southern California

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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AI harm is often behind the scenes and builds over time – a legal scholar explains how the law can adapt to respond

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theconversation.com – Sylvia Lu, Faculty Fellow and Visiting Assistant Professor of Law, University of Michigan – 2024-11-22 07:25:00

One AI harm is pervasive facial recognition, which erodes privacy.
DSCimage/iStock via Getty Images

Sylvia Lu, University of Michigan

As you scroll through your social media feed or let your favorite music app curate the perfect playlist, it may feel like artificial intelligence is improving your life – learning your preferences and serving your needs. But lurking behind this convenient facade is a growing concern: algorithmic harms.

These harms aren’t obvious or immediate. They’re insidious, building over time as AI systems quietly make decisions about your life without you even knowing it. The hidden power of these systems is becoming a significant threat to privacy, equality, autonomy and safety.

AI systems are embedded in nearly every facet of modern life. They suggest what shows and movies you should watch, help employers decide whom they want to hire, and even influence judges to decide who qualifies for a sentence. But what happens when these systems, often seen as neutral, begin making decisions that put certain groups at a disadvantage or, worse, cause real-world harm?

The often-overlooked consequences of AI applications call for regulatory frameworks that can keep pace with this rapidly evolving technology. I study the intersection of law and technology, and I’ve outlined a legal framework to do just that.

Slow burns

One of the most striking aspects of algorithmic harms is that their cumulative impact often flies under the radar. These systems typically don’t directly assault your privacy or autonomy in ways you can easily perceive. They gather vast amounts of data about people — often without their knowledge — and use this data to shape decisions affecting people’s lives.

Sometimes, this results in minor inconveniences, like an advertisement that follows you across websites. But as AI operates without addressing these repetitive harms, they can scale up, leading to significant cumulative damage across diverse groups of people.

Consider the example of social media algorithms. They are ostensibly designed to promote beneficial social interactions. However, behind their seemingly beneficial facade, they silently track users’ clicks and compile profiles of their political beliefs, professional affiliations and personal lives. The data collected is used in systems that make consequential decisions — whether you are identified as a jaywalking pedestrian, considered for a job or flagged as a risk to commit suicide.

Worse, their addictive design traps teenagers in cycles of overuse, leading to escalating mental health crises, including anxiety, depression and self-harm. By the time you grasp the full scope, it’s too late — your privacy has been breached, your opportunities shaped by biased algorithms, and the safety of the most vulnerable undermined, all without your knowledge.

This is what I call “intangible, cumulative harm”: AI systems operate in the background, but their impacts can be devastating and invisible.

Researcher Kumba Sennaar describes how AI systems perpetuate and exacerbate biases.

Why regulation lags behind

Despite these mounting dangers, legal frameworks worldwide have struggled to keep up. In the United States, a regulatory approach emphasizing innovation has made it difficult to impose strict standards on how these systems are used across multiple contexts.

Courts and regulatory bodies are accustomed to dealing with concrete harms, like physical injury or economic loss, but algorithmic harms are often more subtle, cumulative and hard to detect. The regulations often fail to address the broader effects that AI systems can have over time.

Social media algorithms, for example, can gradually erode users’ mental health, but because these harms build slowly, they are difficult to address within the confines of current legal standards.

Four types of algorithmic harm

Drawing on existing AI and data governance scholarship, I have categorized algorithmic harms into four legal areas: privacy, autonomy, equality and safety. Each of these domains is vulnerable to the subtle yet often unchecked power of AI systems.

The first type of harm is eroding privacy. AI systems collect, process and transfer vast amounts of data, eroding people’s privacy in ways that may not be immediately obvious but have long-term implications. For example, facial recognition systems can track people in public and private spaces, effectively turning mass surveillance into the norm.

The second type of harm is undermining autonomy. AI systems often subtly undermine your ability to make autonomous decisions by manipulating the information you see. Social media platforms use algorithms to show users content that maximizes a third party’s interests, subtly shaping opinions, decisions and behaviors across millions of users.

The third type of harm is diminishing equality. AI systems, while designed to be neutral, often inherit the biases present in their data and algorithms. This reinforces societal inequalities over time. In one infamous case, a facial recognition system used by retail stores to detect shoplifters disproportionately misidentified women and people of color.

The fourth type of harm is impairing safety. AI systems make decisions that affect people’s safety and well-being. When these systems fail, the consequences can be catastrophic. But even when they function as designed, they can still cause harm, such as social media algorithms’ cumulative effects on teenagers’ mental health.

Because these cumulative harms often arise from AI applications protected by trade secret laws, victims have no way to detect or trace the harm. This creates a gap in accountability. When a biased hiring decision or a wrongful arrest is made due to an algorithm, how does the victim know? Without transparency, it’s nearly impossible to hold companies accountable.

This UNESCO video features researchers from around the world explaining the issues around the ethics and regulation of AI.

Closing the accountability gap

Categorizing the types of algorithmic harms delineates the legal boundaries of AI regulation and presents possible legal reforms to bridge this accountability gap. Changes I believe would help include mandatory algorithmic impact assessments that require companies to document and address the immediate and cumulative harms of an AI application to privacy, autonomy, equality and safety – before and after it’s deployed. For instance, firms using facial recognition systems would need to evaluate these systems’ impacts throughout their life cycle.

Another helpful change would be stronger individual rights around the use of AI systems, allowing people to opt out of harmful practices and making certain AI applications opt in. For example, requiring an opt-in regime for data processing by firms’ use of facial recognition systems and allowing users to opt out at any time.

Lastly, I suggest requiring companies to disclose the use of AI technology and its anticipated harms. To illustrate, this may include notifying customers about the use of facial recognition systems and the anticipated harms across the domains outlined in the typology.

As AI systems become more widely used in critical societal functions – from health care to education and employment – the need to regulate harms they can cause becomes more pressing. Without intervention, these invisible harms are likely to continue to accumulate, affecting nearly everyone and disproportionately hitting the most vulnerable.

With generative AI multiplying and exacerbating AI harms, I believe it’s important for policymakers, courts, technology developers and civil society to recognize the legal harms of AI. This requires not just better laws, but a more thoughtful approach to cutting-edge AI technology – one that prioritizes civil rights and justice in the face of rapid technological advancement.

The future of AI holds incredible promise, but without the right legal frameworks, it could also entrench inequality and erode the very civil rights it is, in many cases, designed to enhance.The Conversation

Sylvia Lu, Faculty Fellow and Visiting Assistant Professor of Law, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Awkwardness can hit in any social situation – here are a philosopher’s 5 strategies to navigate it with grace

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theconversation.com – Alexandra Plakias, Associate Professor of Philosophy, Hamilton College – 2024-11-22 07:25:00

‘I don’t even know what to say to that.’
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Alexandra Plakias, Hamilton College

The holidays offer many opportunities for awkward moments. Political discussions, of course, hold plenty of potential. But any time opinions differ, where estrangements have caused lingering rifts, or when behaviors veer toward the inappropriate, awkwardness can set in.

Awkwardness is what happens in social interactions when you suddenly find yourself without a script to guide you through. Maybe the situation is new or catches you off guard. Maybe you don’t know what’s expected of you, or you aren’t sure what role you’re playing in the social drama around you. It’s characterized by feelings of self-consciousness, uncertainty and discomfort.

As a philosopher who studies moral psychology, I’m interested in awkwardness because I wanted to understand the ways social discomfort stops people from engaging with difficult topics and challenging conversations. Awkwardness seems to inhibit people, even when their moral values suggest they should speak up. But it has a positive role to play, too – it can alert people to areas where their social norms are lacking or outdated.

People often blame themselves when things take a turn toward the awkward. But awkwardness is really a collective failure – people aren’t awkward, situations are. And they become awkward because you don’t have the resources to navigate your way through tricky social situations.

Awkwardness is often confused with embarrassment, but the two are different in important ways, and so are their remedies. Embarrassment is a response to a personal failing or gaffe, and the right response is to acknowledge it, own it and move on. Because awkwardness is caused by a lack of social guidance, you can try to anticipate and head it off before it happens, or you can respond to it by trying to develop better or clearer social scripts to help you – and others – navigate similar situations in the future.

After researching and writing an entire book on awkwardness, I’ve come to the conclusion that it’s not something we can – or should – avoid altogether. But there are a few strategies people can use to minimize awkwardness and deal with it when it does, inevitably, happen.

1. Know your goals, know your roles

Uncertainty is the oxygen of awkwardness. Before you engage in a potentially awkward or contentious interaction, ask yourself: What do I want to get out of this?

When you’re clear on your goals for the interaction, not only are you better able to perform your role in it, but you’re also giving clearer signals to others, helping them perform their roles in the unfolding social drama.

So, if you’re worried it’ll be awkward when your uncle starts in on his annual political rant, think about what you want the outcome to be. Do you want to convince him he’s wrong? Unlikely to happen. Do you want other family members to feel less anxious? Do you want your own views to be heard?

I’m not suggesting that some forethought will make things go smoothly or guarantee that no one’s feelings will be hurt. But it will help you feel more confident in your ability to navigate toward your desired outcome.

woman bringing pie to a family dinner table
Serving dessert could provide a lifeline to someone looking for a diversion.
Drazen Zigic/iStock via Getty Images Plus

2. There’s no ‘I’ in awkward

Awkward situations breed intense self-consciousness. This is both uncomfortable and counterproductive. By focusing on yourself, you’re not attuned to the people around you or the signals they’re sending – signals that could offer you a pathway out of the awkward situation. So make sure you’re paying attention to the other players in the drama, not just your own discomfort.

3. Plan, coordinate and be explicit

People do so much planning in other areas of their lives, yet they expect social interactions to just flow effortlessly. But like a vacation or a hike in the woods, sometimes a conversation goes better when you approach it with a map. Have some go-to topics or questions at hand.

And you don’t have to go it alone. If you’re worried about broaching a sensitive topic, or interacting with a particularly prickly guest, coordinate with a friend or relative.

If you expect to see someone with whom you have an unresolved relationship – an estranged family member, an old friend you ghosted – try to do some prep work in advance. Emails or letters can give people a chance to process reactions without putting them on the spot.

Even having a scripted activity on deck can make things less awkward. It doesn’t have to be anything formal, like a board game. Just keep some tasks available for guests who might otherwise lurk uncomfortably – like shaking up the salad dressing or putting forks on the table.

4. Laugh it off

If, despite your best efforts, awkwardness does strike, offer people a way out – they’ll probably grab it. This doesn’t need to be momentous; it could be a little joke, a small-talk topic, or even – and only if things get very desperate – knocking a spoon off the table to break the silence.

5. Consider the alternatives

These strategies might help you avoid awkwardness. But take a moment to consider whether you really want to. Awkwardness is the result of social uncertainty; it slows things down and curbs your confidence.

In its absence, other emotions can set in. Having things out in the open can be a relief, but it can also lead to anger, sadness and other feelings that might best be saved for another occasion.

So if things are awkward, it’s worth looking around to see what role that awkwardness is playing, and what might take its place if it’s gone.The Conversation

Alexandra Plakias, Associate Professor of Philosophy, Hamilton College

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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No need to overload your cranberry sauce with sugar this holiday season − a food scientist explains how to cook with fewer added sweeteners

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theconversation.com – Rosemary Trout, Associate Clinical Professor of Culinary Arts & Food Science, Drexel University – 2024-11-22 07:24:00

Fall means cranberry season − and sweet seasonal holiday dishes.
AP Photo/Sergei Grits

Rosemary Trout, Drexel University

The holidays are full of delicious and indulgent food and drinks. It’s hard to resist dreaming about cookies, specialty cakes, rich meats and super saucy side dishes.

Lots of the healthy raw ingredients used in holiday foods can end up overshadowed by sugar and starch. While adding extra sugar may be tasty, it’s not necessarily good for metabolism. Understanding the food and culinary science behind what you’re cooking means you can make a few alterations to a recipe and still have a delicious dish that’s not overloaded with sugar.

Particularly, if you’re a person living with Type 1 diabetes, the holidays may come with an additional layer of stress and wild blood glucose levels. It’s no time for despair though – it is the holidays, after all.

Cranberries are one seasonal, tasty fruit that can be modified in recipes to be more Type 1 diabetic-friendly – or friendly to anyone looking for a sweet dish without the extra sugar.

I am a food scientist and a Type 1 diabetic. Understanding food composition, ingredient interactions and metabolism has been a literal lifesaver for me.

Type 1 diabetes defined

Type 1 diabetes is all day every day, with no breaks during sleep, no holidays or weekends off, no remission and no cure. Type 1 diabetics don’t make insulin, a hormone that is required to live that promotes the uptake of glucose, or sugar, into cells. The glucose in your cells then supplies your body with energy at the molecular level.

Consequently, Type 1 diabetics take insulin by injection, or via an insulin pump attached to their bodies, and hope that it works well enough to stabilize blood sugar and metabolism, minimize health complications over time and keep us alive.

Type 1 diabetics mainly consider the type and amount of carbohydrates in foods when figuring out how much insulin to take, but they also need to understand the protein and fat interactions in food to dose, or bolus, properly.

In addition to insulin, Type 1 diabetics don’t make another hormone, amylin, which slows gastric motility. This means food moves more quickly through our digestive tract, and we often feel very hungry. Foods that are high in fat, proteins and fiber can help to stave off hunger for a while.

Cranberries, a seasonal treat

Cranberries are native to North America and grow well in the Northeastern and Midwestern states, where they are in season between late September and December. They’re a staple on holiday tables all over the country.

A bowl of cranberries with the zest of an orange on top.
Cranberries are a classic Thanksgiving side dish, but cranberry sauce tends to contain a lot of sugar.
bhofack2/iStock via Getty Images

One cup of whole, raw cranberries contains 190 calories. They are 87% water, with trace amounts of protein and fat, 12 grams of carbohydrates and just over 4 grams of soluble fiber. Soluble fiber combines well with water, which is good for digestive health and can slow the rise of blood glucose.

Cranberries are high in potassium, which helps with electrolyte balance and cell signaling, as well as other important nutrients such as antioxidants, beta-carotene and vitamin C. They also contain vitamin K, which helps with healthy blood clotting.

Cranberries’ flavor and aroma come from compounds in the fruit such as cinnamates that add cinnamon notes, vanillin for hints of vanilla, benzoates and benzaldehyde, which tastes like almonds.

Cranberries are high in pectin, a soluble starch that forms a gel and is used as a setting agent in making jams and jellies, which is why they thicken readily with minimal cooking. Their beautiful red jewel-tone color is from a class of compounds called anthocyanins and proanthocyanidins, which are associated with treating some types of infection.

They also contain phenolics, which are protective compounds produced by the plant. These compounds, which look like rings at the molecular level, interact with proteins in your saliva to produce a dry, astringent sensation that makes your mouth pucker. Similarly, a compound called benzoic acid naturally found in cranberries adds to the fruit’s sourness.

These chemical ingredients make them extremely sour and bitter, and difficult to consume raw. To mitigate these flavors and effects, most cranberry recipes call for lots of sugar.

All that extra sugar can make cranberry dishes hard to consume for Type 1 diabetics, because the sugars cause a rapid rise in blood glucose.

Cranberries without sugar?

Type 1 diabetics – or anyone who wants to reduce the added sugars they’re consuming – can try a few culinary tactics to lower their sugar intake while still enjoying this holiday treat.

Don’t cook your cranberries much longer after they pop. You’ll still have a viscous cranberry liquid without the need for as much sugar, since cooking concentrates some of the bitter compounds, making them more pronounced in your dish.

A line of spoons, each heaped with a pile of powdered spice.
Adding spices to your cranberries can enhance the dish’s flavor without extra sugar.
klenova/iStock via Getty Images

Adding cinnamon, clove, cardamom, nutmeg and other warming spices gives the dish a depth of flavor. Adding heat with a spicy chili pepper can make your cranberry dish more complex while reducing sourness and astringency. Adding salt can reduce the cranberries’ bitterness, so you won’t need lots of sugar.

For a richer flavor and a glossy quality, add butter. Butter also lubricates your mouth, which tends to compliment the dish’s natural astringency. Other fats such as heavy cream or coconut oil work, too.

Adding chopped walnuts, almonds or hazelnuts can slow glucose absorption, so your blood glucose may not spike as quickly. Some new types of sweeteners, such as allulose, taste sweet but don’t raise blood sugar, requiring minimal to no insulin. Allulose has GRAS – generally regarded as safe – status in the U.S., but it isn’t approved as an additive in Europe.

This holiday season you can easily cut the amount of sugar added to your cranberry dishes and get the health benefits without a blood glucose spike.The Conversation

Rosemary Trout, Associate Clinical Professor of Culinary Arts & Food Science, Drexel University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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