Ultrasensitive matter-wave interferometer uses delocalization and entanglement to detect tiny accelerations.
Theoretical physicist and author, Julian Barbour, talks to us about why time is an illusion and what this means for the quantum mechanics of the universe.
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Quantum researchers require access to different types of quantum hardware from digital, also known as gate-based, quantum processing units (QPUs) to analog devices that are capable of addressing specific problems that are hard to solve using classical computers. Today, Amazon Braket, the quantum computing service from AWS, continues to deliver on its commitment to provide that choice by launching Aquila, pictured in Figure 1 below, a new neutral-atom QPU from QuEra Computing with up to 256 qubits. As a special purpose device designed for solving optimization problems and simulating quantum phenomena in nature, it enables researchers to explore a new analog paradigm of quantum computing.
Circa 2021 face_with_colon_three
In the cosmological context, space can get similarly stuck in a false vacuum state. A speck of false vacuum will occasionally relax into true vacuum (likely through a random quantum event), and this true vacuum will balloon outward as a swelling bubble, feasting on the false vacuum’s excess energy, in a process called false vacuum decay. It’s this process that may have started our cosmos with a bang. “A vacuum bubble could have been the first event in the history of our universe,” said Hiranya Peiris, a cosmologist at University College London.
But physicists struggle mightily to predict how vacuum bubbles behave. A bubble’s future depends on countless minute details that add up. Bubbles also change rapidly — their walls approach the speed of light as they fly outward — and feature quantum mechanical randomness and waviness. Different assumptions about these processes give conflicting predictions, with no way to tell which ones might resemble reality. It’s as though “you’ve taken a lot of things that are just very hard for physicists to deal with and mushed them all together and said, ‘Go ahead and figure out what’s going on,’” Braden said.
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“We do not use any ancilla qubits,” Yan says. “Instead, we use ancilla states.”
In the new study, the scientists implemented quantum AND gates on a superconducting quantum processor with tunable-coupling architecture. Google also employs this architecture with its quantum computers, and IBM plans to start using it in 2023.
“We think that our scheme is well-suited for superconducting qubit systems where ancilla states are abundant and easy to access,” Yan says.
Error-prone qubits mean quantum systems do not yet surpass classical methods.
In a talk at the Massachusetts Institute of Technology in 1981, Richard Feynman spoke about ‘simulating physics with computers’. This was already being done at the time, but Feynman said he wanted to talk ‘about the possibility that there is to be an exact simulation, that the computer will do exactly the same as nature.’ But as nature is quantum-mechanical, he pointed out, what you need for that is a quantum computer.
The rest is history – but history still in the making. When I recently asked David Deutsch, the visionary physicist who in 1985 laid out what quantum computing might look like, whether he was surprised at how quickly the idea became a practical technology, he replied with characteristic terseness: ‘It hasn’t.’ You can see his point. Sure, in October President Joe Biden visited IBM’s new quantum data centre in Poughkeepsie, New York, to see an entire room filled with the company’s quantum computers. And on 9 November IBM announced its 433-quantum-bit (qubit) Osprey processor, although it seems only yesterday that we were getting excited at Google’s 53-qubit Sycamore chip – with which the Google team claimed in 2016 to demonstrate ‘quantum supremacy’, meaning that it could perform a calculation in a few days that would take the best classical computer many millennia.1 This claim has since been disputed.
Quantum superposition is not just a property of subatomic particles but also of the most massive objects in the universe. That is the conclusion of four theoretical physicists in Australia and Canada who calculated the hypothetical response of a particle detector placed some distance from a black hole. The researchers say the detector would see novel signs of superimposed space–times, implying that the black hole may have two different masses simultaneously.
Black holes are formed when extremely massive objects like stars collapse to a singularity – a point of infinite density. The gravitational field of a black hole is so great that nothing can escape its clutches, not even light. This creates a spherical region of space around the singularity entirely cut off from the rest of the universe and bounded by what is known as an event horizon.
An active area of research into the physics of black holes seeks to develop a consistent theory of quantum gravity. This is an important goal of theoretical physics that would reconcile quantum mechanics and Einstein’s general theory of relativity. In particular, by considering black holes in quantum superposition, physicists hope to gain insights into the quantum nature of space–time.
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Quantum circuits still can’t outperform classical ones when simulating molecules.
Quantum computers promise to directly simulate systems governed by quantum principles, such as molecules or materials, since the quantum bits themselves are quantum objects. Recent experiments have demonstrated the power of these devices when performing carefully chosen tasks. But a new study shows that for problems of real-world interest, such as calculating the energy states of a cluster of atoms, quantum simulations are no more accurate than those of classical computers [1]. The results offer a benchmark for judging how close quantum computers are to becoming useful tools for chemists and materials scientists.
Richard Feynman proposed the idea of quantum computers in 1982, suggesting they might be used to calculate the properties of quantum matter. Today, quantum processors are available with several hundred quantum bits (qubits), and some can, in principle, represent quantum states that are impossible to encode in any classical device. The 53-qubit Sycamore processor developed by Google has demonstrated the potential to perform calculations in a few days that would take many millennia on current classical computers [2]. But this “quantum advantage” is achieved only for selected computational tasks that play to these devices’ strengths. How well do such quantum computers fare for the sorts of everyday challenges that researchers studying molecules and materials actually wish to solve?
