Algorithmic Shadow Spectroscopy

We present shadow spectroscopy as a simulator-agnostic quantum algorithm for estimating energy gaps using very few circuit repetitions (shots) and no extra resources (ancilla qubits) beyond performing time evolution and measurements. The approach builds on the fundamental feature that every observable property of a quantum system must evolve according to the same harmonic components: we can reveal them by postprocessing classical shadows of time-evolved quantum states to extract a large number of time-periodic signals <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><a:msub><a:mi>N</a:mi><a:mi>o</a:mi></a:msub><a:mo>∝</a:mo><a:msup><a:mn>10</a:mn><a:mn>8</a:mn></a:msup></a:math>, whose frequencies correspond to Hamiltonian energy differences with precision limited as <d:math xmlns:d="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><d:mi>ϵ</d:mi><d:mo>∝</d:mo><d:mn>1</d:mn><d:mo>/</d:mo><d:mi>T</d:mi></d:math> for simulation time <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><g:mi>T</g:mi></g:math>. We provide strong analytical guarantees that (a) quantum resources scale as <j:math xmlns:j="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><j:mi>O</j:mi><j:mo stretchy="false">(</j:mo><j:mi>log</j:mi><j:mo></j:mo><j:msub><j:mi>N</j:mi><j:mi>o</j:mi></j:msub><j:mo stretchy="false">)</j:mo></j:math>, while the classical computational complexity is linear <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><o:mi>O</o:mi><o:mo stretchy="false">(</o:mo><o:msub><o:mi>N</o:mi><o:mi>o</o:mi></o:msub><o:mo stretchy="false">)</o:mo></o:math>, (b) the signal-to-noise ratio increases with the number of processed signals as <t:math xmlns:t="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><t:mo>∝</t:mo><t:msqrt><t:msub><t:mi>N</t:mi><t:mi>o</t:mi></t:msub></t:msqrt></t:math>, and (c) spectral peak positions are immune to reasonable levels of noise. We demonstrate our approach on model spin systems and the excited-state conical intersection of molecular <w:math xmlns:w="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><w:msub><w:mi>CH</w:mi><w:mn>2</w:mn></w:msub></w:math> and verify that our method is indeed intuitively easy to use in practice, robust against gate noise, amiable to a new type of algorithmic-error mitigation technique, and uses relatively few shots given a reasonable initial state is supplied—we demonstrate that even 10 shots per time step can be sufficient. Finally, we measured a high-quality, experimental shadow spectrum of a spin chain on readily available IBM quantum computers, achieving the same precision as in noise-free simulations without using any advanced error mitigation, and verified scalability in tensor-network simulations of up to 100-qubit systems.

Authors

Institutions

Publication Details

Journal
PRX Quantum
Published
2025-03-17
DOI
https://doi.org/10.1103/prxquantum.6.010352
Citations
12
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
29.77

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Algorithmic Shadow Spectroscopy

Hans Hon Sang Chan, Matthew L. Goh, Richard J. Meister, Bálint Koczor
12 citations
PRX Quantum
Neural Networks and Reservoir Computing
29.77
article

Algorithmic Shadow Spectroscopy

Hans Hon Sang Chan, Matthew L. Goh, Richard J. Meister, Bálint Koczor
article en
12 citations

Abstract

We present shadow spectroscopy as a simulator-agnostic quantum algorithm for estimating energy gaps using very few circuit repetitions (shots) and no extra resources (ancilla qubits) beyond performing time evolution and measurements. The approach builds on the fundamental feature that every observable property of a quantum system must evolve according to the same harmonic components: we can reveal them by postprocessing classical shadows of time-evolved quantum states to extract a large number of time-periodic signals <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><a:msub><a:mi>N</a:mi><a:mi>o</a:mi></a:msub><a:mo>∝</a:mo><a:msup><a:mn>10</a:mn><a:mn>8</a:mn></a:msup></a:math>, whose frequencies correspond to Hamiltonian energy differences with precision limited as <d:math xmlns:d="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><d:mi>ϵ</d:mi><d:mo>∝</d:mo><d:mn>1</d:mn><d:mo>/</d:mo><d:mi>T</d:mi></d:math> for simulation time <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><g:mi>T</g:mi></g:math>. We provide strong analytical guarantees that (a) quantum resources scale as <j:math xmlns:j="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><j:mi>O</j:mi><j:mo stretchy="false">(</j:mo><j:mi>log</j:mi><j:mo></j:mo><j:msub><j:mi>N</j:mi><j:mi>o</j:mi></j:msub><j:mo stretchy="false">)</j:mo></j:math>, while the classical computational complexity is linear <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><o:mi>O</o:mi><o:mo stretchy="false">(</o:mo><o:msub><o:mi>N</o:mi><o:mi>o</o:mi></o:msub><o:mo stretchy="false">)</o:mo></o:math>, (b) the signal-to-noise ratio increases with the number of processed signals as <t:math xmlns:t="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><t:mo>∝</t:mo><t:msqrt><t:msub><t:mi>N</t:mi><t:mi>o</t:mi></t:msub></t:msqrt></t:math>, and (c) spectral peak positions are immune to reasonable levels of noise. We demonstrate our approach on model spin systems and the excited-state conical intersection of molecular <w:math xmlns:w="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><w:msub><w:mi>CH</w:mi><w:mn>2</w:mn></w:msub></w:math> and verify that our method is indeed intuitively easy to use in practice, robust against gate noise, amiable to a new type of algorithmic-error mitigation technique, and uses relatively few shots given a reasonable initial state is supplied—we demonstrate that even 10 shots per time step can be sufficient. Finally, we measured a high-quality, experimental shadow spectrum of a spin chain on readily available IBM quantum computers, achieving the same precision as in noise-free simulations without using any advanced error mitigation, and verified scalability in tensor-network simulations of up to 100-qubit systems.

PRX QuantumVol. 6(1)
University of Oxford (GB), Quantum Motion Technologies (United Kingdom) (GB)
Croucher Foundation, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
29.77
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.