Sergio Boixo
A5065681671- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum many-body systems
- Quantum and electron transport phenomena
- Quantum Mechanics and Applications
- Neural Networks and Reservoir Computing
- Cold Atom Physics and Bose-Einstein Condensates
- Advancements in Semiconductor Devices and Circuit Design
- Neural Networks and Applications
- Advanced Thermodynamics and Statistical Mechanics
- Physics of Superconductivity and Magnetism
- Quantum-Dot Cellular Automata
- Theoretical and Computational Physics
- Parallel Computing and Optimization Techniques
- Machine Learning in Materials Science
- Semiconductor materials and devices
- Computational Physics and Python Applications
- Low-power high-performance VLSI design
- Statistical Mechanics and Entropy
- Stochastic Gradient Optimization Techniques
- Spectroscopy and Quantum Chemical Studies
- Computability, Logic, AI Algorithms
- Error Correcting Code Techniques
- Quantum, superfluid, helium dynamics
- Advanced Data Storage Technologies
Google (United States)
2016-2025
University of California, Riverside
2022
University of Southern California
2012-2014
Harvard University
2011-2014
Harvard University Press
2012-2013
University of Oxford
2012
California Institute of Technology
2009-2011
Los Alamos National Laboratory
2007-2008
University of New Mexico
2006-2008
As the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations fermionic systems remain one most promising directions. Here, we perform a series chemistry largest which involved dozen qubits, 78 two-qubit gates, and 114 one-qubit gates. We model binding energy ${\rm H}_6$, H}_8$, H}_{10}$ H}_{12}$ chains as well isomerization diazene. also demonstrate error-mitigation strategies based on $N$-representability dramatically improve effective...
Abstract Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum correction 1,2 offers a path to algorithmically relevant by encoding logical qubits within many qubits, for which increasing the number of enhances protection against errors. However, introducing more also increases sources, so density errors must be sufficiently low performance improve code size. Here we report measurement qubit scaling across several sizes, and...
Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized circuit with classical optimization loop. Such hybrid quantum-classical algorithms are popular applications in simulation, optimization, and machine learning. Due to its simplicity hardware efficiency, random circuits often proposed as initial guesses exploring the space of states. We show that exponential dimension Hilbert gradient estimation complexity make this choice unsuitable run...
The development of small-scale digital and analog quantum devices raises the question how to fairly assess compare computational power classical devices, detect speedup. Here we show define measure speedup in various scenarios, avoid pitfalls that might mask or fake We illustrate our discussion with data from a randomized benchmark test on D-Wave Two device up 503 qubits. Comparing performance random spin glass instances limited precision simulated annealers, find no evidence when entire set...
Abstract The use of quantum computing for machine learning is among the most exciting prospective applications technologies. However, tasks where data provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems are classically hard to compute easily predicted by classical machines from data. Using rigorous prediction error bounds as a foundation, develop methodology assessing potential advantage in tight asymptotically and...
A key step toward demonstrating a quantum system that can address difficult problems in physics and chemistry will be performing computation beyond the capabilities of any classical computer, thus achieving so-called supremacy. In this study, we used nine superconducting qubits to demonstrate promising path By individually tuning qubit parameters, were able generate thousands distinct Hamiltonian evolutions probe output probabilities. The measured probabilities obey universal distribution,...
Quantum annealing is a quantum enhanced heuristic optimization algorithm that exploits tunneling. New work shows it can significantly outperform its classical analog (simulated annealing) as well the most popular for simulating (quantum Monte Carlo).
The discovery of topological order has revolutionized the understanding quantum matter in modern physics and provided theoretical foundation for many error correcting codes. Realizing topologically ordered states proven to be extremely challenging both condensed synthetic systems. Here, we prepare ground state toric code Hamiltonian using an efficient circuit on a superconducting processor. We measure entanglement entropy near expected value $\ln2$, simulate anyon interferometry extract...
We develop generalized bounds for quantum single-parameter estimation problems which the coupling to parameter is described by intrinsic multisystem interactions. For a Hamiltonian with $k$-system parameter-sensitive terms, limit scales as $1/{N}^{k}$, where $N$ number of systems. These limits remain valid when augmented any parameter-independent interaction among systems and adaptive measurements via ancillas are allowed.
Quantum discord quantifies nonclassical correlations beyond the standard classification of quantum states into entangled and unentangled. Although it has received considerable attention, still lacks any precise interpretation in terms some protocol which features are relevant. Here we give its first information-theoretic operational meaning entanglement consumption an extended quantum-state-merging protocol. We further relate asymmetry with performance imbalance state merging dense coding.
Realizing the potential of quantum computing requires sufficiently low logical error rates1. Many applications call for rates as 10-15 (refs. 2-9), but state-of-the-art platforms typically have physical near 10-3 10-14). Quantum correction15-17 promises to bridge this divide by distributing information across many qubits in such a way that errors can be detected and corrected. Errors on encoded qubit state exponentially suppressed number grows, provided are below certain threshold stable...
Abstract Emerging reinforcement learning techniques using deep neural networks have shown great promise in control optimization. They harness non-local regularities of noisy trajectories and facilitate transfer between tasks. To leverage these powerful capabilities for quantum optimization, we propose a new framework to simultaneously optimize the speed fidelity computation against both leakage stochastic errors. For broad family two-qubit unitary gates that are important simulation...
D-wave computers are designed to implement a single quantum algorithm called annealing. Are these really quantum? Researchers have found strong evidence that qubits in processor running the entangled mechanically.
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models classical or quantum data. This framework offers high-level abstractions design and training both discriminative generative under supports high-performance circuit simulators. provide overview software architecture building blocks through several examples review theory neural networks. illustrate TFQ functionalities via basic applications including supervised learning...
We develop from first principles Markovian master equations suited for studying the time evolution of a system evolving adiabatically while coupled weakly to thermal bath. derive two sets in adiabatic limit, one using rotating wave (secular) approximation that results equation Lindblad form, other without but not form. The make markedly different predictions depending on whether or Lamb shift is included. Our analysis keeps track various and energy scales associated with approximations we...
Superconducting qubits are an attractive platform for quantum computing since they have demonstrated high-fidelity gates and extensibility to modest system sizes. Nonetheless, outstanding challenge is stabilizing their energy-relaxation times, which can fluctuate unpredictably in frequency time. Here, we use as spectral temporal probes of individual two-level-system defects provide direct evidence that responsible the largest fluctuations. This research lays foundation qubit performance...
Quantum algorithms offer a dramatic speedup for computational problems in material science and chemistry. However, any near-term realizations of these will need to be optimized fit within the finite resources offered by existing noisy hardware. Here, taking advantage adjustable coupling gmon qubits, we demonstrate continuous two-qubit gate set that can provide threefold reduction circuit depth as compared standard decomposition. We implement two families: an imaginary swap-like (iSWAP-like)...
Abstract Quantum many-body systems display rich phase structure in their low-temperature equilibrium states 1 . However, much of nature is not thermal equilibrium. Remarkably, it was recently predicted that out-of-equilibrium can exhibit novel dynamical phases 2–8 may otherwise be forbidden by thermodynamics, a paradigmatic example being the discrete time crystal (DTC) 7,9–15 Concretely, defined periodically driven many-body-localized (MBL) via concept eigenstate order 7,16,17 In...
Interaction in quantum systems can spread initially localized information into the many degrees of freedom entire system. Understanding this process, known as scrambling, is key to resolving various conundrums physics. Here, by measuring time-dependent evolution and fluctuation out-of-time-order correlators, we experimentally investigate dynamics scrambling on a 53-qubit processor. We engineer circuits that distinguish two mechanisms associated with operator spreading entanglement, observe...