Measurement as a Shortcut to Long-Range Entangled Quantum Matter

Lu, Tsung-Cheng; Lessa, Leonardo A.; Kim, Isaac H.; Hsieh, Timothy H.

doi:10.1103/prxquantum.3.040337

Cited by 60 publications

(5 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the classification task, we expanded on the previous work 26,27 of distinguishing the Symmetry Protected Topological (SPT) phase 28 by performing the classification task in a more general setting and increasing the system size up to 44 qubits. Also, with the help of a measurement-assisted state preparation method 29 , which enabled the generation of suitable training data, we demonstrated successful phase classification between topologically ordered and trivial phases of a system comprising as many as 25 qubits, thereby confirming the scalability and applicability of the ML algorithms. We conducted our experiments on a device consisting of superconducting qubits provided by IBM.…”

mentioning

confidence: 59%

Machine learning on quantum experimental data toward solving quantum many-body problems

Kim,

Cho

2023

Preprint

View full text Add to dashboard Cite

Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds potential for unveiling obscure patterns. Although this hybrid approach extends the class of efficiently solvable problems compared to using only classical computers, this approach has been realized for solving restricted problems because of the prevalence of noise in current quantum computers. Here, we extend the applicability of the hybrid approach to problems of interest in many-body physics, such as predicting the properties of the ground state of a given Hamiltonian and classifying quantum phases. By performing experiments with various error-reducing procedures on superconducting quantum hardware with 127 qubits, we managed to acquire refined data from the quantum computer. This enabled us to demonstrate the successful implementation of classical ML algorithms for systems with up to 44 qubits. Our results verify the scalability and effectiveness of the classical ML algorithms for processing quantum experimental data.

show abstract

mentioning

confidence: 59%

Machine learning on quantum experimental data toward solving quantum many-body problems

Kim,

Cho

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Namely, only a single round of feed-forward and low-error gates suffices to deterministically prepare such states. Moreover, multiple rounds of measurements and feed-forward can access even more exotic and powerful non-Abelian states [13][14][15][16][17] which open up new avenues for fault tolerant quantum information processing.…”

Section: Discussionmentioning

confidence: 99%

“…The deterministic preparation of an excitation-free state is important for the quantum simulation of topologically ordered systems with non-error corrected devices, but is also a common prerequisite for quantum error correction protocols that realize universal gate sets 12 . Moreover, feedforward is indispensable for the efficient preparation of certain non-Abelian states involving multiple layers of measurement [13][14][15][16][17][18] .…”

mentioning

confidence: 99%

Topological order from measurements and feed-forward on a trapped ion quantum computer

Iqbal,

Tantivasadakarn,

Gatterman

et al. 2024

Commun Phys

View full text Add to dashboard Cite

Quantum systems evolve in time in one of two ways: through the Schrödinger equation or wavefunction collapse. So far, deterministic control of quantum many-body systems in the lab has focused on the former, due to the probabilistic nature of measurements. This imposes serious limitations: preparing long-range entangled states, for example, requires extensive circuit depth if restricted to unitary dynamics. In this work, we use mid-circuit measurement and feed-forward to implement deterministic non-unitary dynamics on Quantinuum’s H1 programmable ion-trap quantum computer. Enabled by these capabilities, we demonstrate a constant-depth procedure for creating a toric code ground state in real-time. In addition to reaching high stabilizer fidelities, we create a non-Abelian defect whose presence is confirmed by transmuting anyons via braiding. This work clears the way towards creating complex topological orders in the lab and exploring deterministic non-unitary dynamics via measurement and feed-forward.

show abstract

“…This theorem also has strong implications on the ease with which many tasks, such as preparing highly entangled quantum states, can be achieved using hybrid protocols involving both unitary dynamics and measurement. This has been a subject of intense recent interest [72][73][74][75][76][77][78][79].…”

Section: Theorem 511 (Speed Limit In Quantum Dynamics With Measuremen...mentioning

confidence: 99%

Speed limits and locality in many-body quantum dynamics

(Anthony) Chen,

Lucas,

Yin

2023

Rep. Prog. Phys.

View full text Add to dashboard Cite

We review the mathematical speed limits on quantum information processing in many-body systems. After the proof of the Lieb-Robinson Theorem in 1972, the past two decades have seen substantial developments in its application to other questions, such as the simulatability of quantum systems on classical or quantum computers, the generation of entanglement, and even the properties of ground states of gapped systems. Moreover, Lieb-Robinson bounds have been extended in non-trivial ways, to demonstrate speed limits in systems with power-law interactions or interacting bosons, and even to prove notions of locality that arise in cartoon models for quantum gravity with all-to-all interactions. We overview the progress which has occurred, highlight the most promising results and techniques, and discuss some central outstanding questions which remain open. To help bring newcomers to the field up to speed, we provide self-contained proofs of the field's most essential results.

show abstract

Measurement as a Shortcut to Long-Range Entangled Quantum Matter

Cited by 60 publications

References 55 publications

Machine learning on quantum experimental data toward solving quantum many-body problems

Machine learning on quantum experimental data toward solving quantum many-body problems

Topological order from measurements and feed-forward on a trapped ion quantum computer

Speed limits and locality in many-body quantum dynamics

Contact Info

Product

Resources

About