Developed efficient algorithms to learn sparse quantum states using only single-qubit measurements with polynomial sample complexity, enabling practical state tomography of entangled systems on current NISQ devices without entangling gates.
#QuantumTomography #QuantumAlgorithms #Research
#QuantumTomography #QuantumAlgorithms #Research
Learning Sparse Quantum States from Single-Qubit Measurements
iq.fp2.dev
September 29, 2026 at 5:39 AM
Developed efficient algorithms to learn sparse quantum states using only single-qubit measurements with polynomial sample complexity, enabling practical state tomography of entangled systems on current NISQ devices without entangling gates.
#QuantumTomography #QuantumAlgorithms #Research
#QuantumTomography #QuantumAlgorithms #Research
New tomography method enables polynomial-sample reconstruction of mixed quantum states with extensive entanglement and magic by exploiting hidden block structure, surpassing prior methods limited to restricted state classes.
#QuantumTomography #QuantumInformation #Research
#QuantumTomography #QuantumInformation #Research
Efficient Tomography of Highly Entangled Mixed Quantum States
arxiv.org
September 24, 2026 at 4:22 AM
New tomography method enables polynomial-sample reconstruction of mixed quantum states with extensive entanglement and magic by exploiting hidden block structure, surpassing prior methods limited to restricted state classes.
#QuantumTomography #QuantumInformation #Research
#QuantumTomography #QuantumInformation #Research
Quantum network tomography requires probe path constraints beyond measurement completeness. Novel FIM analysis reveals that topology and monitor placement critically determine link parameter identifiability.
#QuantumNetworking #QuantumTomography #Research
#QuantumNetworking #QuantumTomography #Research
Identifiability in Quantum State, Process, and Network Tomography via Fisher Information Matrix
iq.fp2.dev
September 16, 2026 at 1:07 PM
Quantum network tomography requires probe path constraints beyond measurement completeness. Novel FIM analysis reveals that topology and monitor placement critically determine link parameter identifiability.
#QuantumNetworking #QuantumTomography #Research
#QuantumNetworking #QuantumTomography #Research
Novel algorithms achieve near-optimal complexity for learning k-sparse quantum states: Õ(k/ε) samples and Õ(kn/ε) time for pure states, with extension to mixed states via purification.
#QuantumAlgorithms #QuantumTomography #Research
#QuantumAlgorithms #QuantumTomography #Research
Optimal Algorithms for Sparse Quantum State Tomography
arxiv.org
September 15, 2026 at 9:28 AM
Novel algorithms achieve near-optimal complexity for learning k-sparse quantum states: Õ(k/ε) samples and Õ(kn/ε) time for pure states, with extension to mixed states via purification.
#QuantumAlgorithms #QuantumTomography #Research
#QuantumAlgorithms #QuantumTomography #Research
Physicists develop a quantum tomography technique proving entanglement in mixed-spin hadrons can be certified from incomplete experimental data, enabling direct studies of how quantum spin correlations evolve during QCD hadronization.
#QuantumEntanglement #QuantumTomography #Research
#QuantumEntanglement #QuantumTomography #Research
Quantum Tomography of Mixed-Spin Hadronic Systems
arxiv.org
September 11, 2026 at 4:24 AM
Physicists develop a quantum tomography technique proving entanglement in mixed-spin hadrons can be certified from incomplete experimental data, enabling direct studies of how quantum spin correlations evolve during QCD hadronization.
#QuantumEntanglement #QuantumTomography #Research
#QuantumEntanglement #QuantumTomography #Research
Researchers proved optimal sample complexity for learning low-rank quantum states, demonstrating that jointly measuring t samples improves over single-sample tomography by factor √t, with matching algorithmic and information-theoretic bounds.
#QuantumTomography #QuantumAlgorithms #Research
#QuantumTomography #QuantumAlgorithms #Research
Sample-Optimal Quantum State Tomography via Bounded-Sample Joint Measurements
arxiv.org
September 10, 2026 at 9:08 AM
Researchers proved optimal sample complexity for learning low-rank quantum states, demonstrating that jointly measuring t samples improves over single-sample tomography by factor √t, with matching algorithmic and information-theoretic bounds.
#QuantumTomography #QuantumAlgorithms #Research
#QuantumTomography #QuantumAlgorithms #Research
Resolves open question in quantum state tomography by proving tight lower bounds for protocols where each measurement acts on k copies, removing earlier restrictions and determining optimal copy complexity.
#QuantumInformationTheory #QuantumTomography #Research
#QuantumInformationTheory #QuantumTomography #Research
Tight Lower Bounds for Quantum State Tomography with Limited Entanglement
iq.fp2.dev
September 9, 2026 at 5:34 AM
Resolves open question in quantum state tomography by proving tight lower bounds for protocols where each measurement acts on k copies, removing earlier restrictions and determining optimal copy complexity.
#QuantumInformationTheory #QuantumTomography #Research
#QuantumInformationTheory #QuantumTomography #Research
New algorithms learn tensor network quantum states efficiently using graph parameters like cutwidth and tree-cutwidth. Bounds sample and computational complexity of state tomography, with applications to quantum simulation and verification.
#QuantumAlgorithms #TensorNetworks #QuantumTomography
#QuantumAlgorithms #TensorNetworks #QuantumTomography
Parameterised Graph Theory for Tensor Networks: Entanglement Rerouting and Quantum State Tomography
iq.fp2.dev
September 4, 2026 at 7:02 AM
New algorithms learn tensor network quantum states efficiently using graph parameters like cutwidth and tree-cutwidth. Bounds sample and computational complexity of state tomography, with applications to quantum simulation and verification.
#QuantumAlgorithms #TensorNetworks #QuantumTomography
#QuantumAlgorithms #TensorNetworks #QuantumTomography
Novel CGAN approach using differentiable Cholesky layers overcomes computational scaling bottleneck in quantum tomography, enabling high-fidelity state and process reconstruction for NISQ devices without iterative optimization.
#QuantumML #QuantumTomography #Research
#QuantumML #QuantumTomography #Research
Physics-Constrained Generative Learning for Quantum State and Process Tomography
arxiv.org
August 31, 2026 at 3:15 AM
Novel CGAN approach using differentiable Cholesky layers overcomes computational scaling bottleneck in quantum tomography, enabling high-fidelity state and process reconstruction for NISQ devices without iterative optimization.
#QuantumML #QuantumTomography #Research
#QuantumML #QuantumTomography #Research
Study reveals the unextended Petz recovery map fails at quantum state tomography, but an extended version using candidate state ensembles recovers Bayesian tomography structure, bridging quantum retrodiction and inference theory.
#QuantumTomography #QuantumRetrodiction #Research
#QuantumTomography #QuantumRetrodiction #Research
Tomographic Limits of the Petz Recovery Map
arxiv.org
August 24, 2026 at 2:55 AM
Study reveals the unextended Petz recovery map fails at quantum state tomography, but an extended version using candidate state ensembles recovers Bayesian tomography structure, bridging quantum retrodiction and inference theory.
#QuantumTomography #QuantumRetrodiction #Research
#QuantumTomography #QuantumRetrodiction #Research
Researchers disproved a key conjecture about fractional colorings in quantum shadow tomography, proving fractional chromatic numbers scale worse than O(ε⁻²)—with implications for quantum measurement efficiency protocols.
#QuantumTomography #QuantumInformation #Research
#QuantumTomography #QuantumInformation #Research
Counterexamples to the Fractional Coloring Conjecture for Triply Efficient Shadow Tomography
iq.fp2.dev
August 21, 2026 at 4:30 AM
Researchers disproved a key conjecture about fractional colorings in quantum shadow tomography, proving fractional chromatic numbers scale worse than O(ε⁻²)—with implications for quantum measurement efficiency protocols.
#QuantumTomography #QuantumInformation #Research
#QuantumTomography #QuantumInformation #Research
Novel interferometric method enables complete characterization of quantum operations on high-dimensional photonic states without detecting the transformed photon, using path identity principle.
#QuantumTomography #PhotonicQudits #Research
#QuantumTomography #PhotonicQudits #Research
High-Dimensional Quantum Process Tomography Without Photon Detection
arxiv.org
August 4, 2026 at 4:45 AM
Novel interferometric method enables complete characterization of quantum operations on high-dimensional photonic states without detecting the transformed photon, using path identity principle.
#QuantumTomography #PhotonicQudits #Research
#QuantumTomography #PhotonicQudits #Research
Normalizing flows enable efficient reconstruction of quantum states with improved accuracy for non-classical systems. QST-Flow achieves better results than traditional methods while handling noisy real-world measurements.
#QuantumTomography #MachineLearning #QuantumComputing
#QuantumTomography #MachineLearning #QuantumComputing
QST-Flow: Machine Learning Framework for Scalable Quantum State Tomography
iq.fp2.dev
August 1, 2026 at 1:05 PM
Normalizing flows enable efficient reconstruction of quantum states with improved accuracy for non-classical systems. QST-Flow achieves better results than traditional methods while handling noisy real-world measurements.
#QuantumTomography #MachineLearning #QuantumComputing
#QuantumTomography #MachineLearning #QuantumComputing
Protocol reduces tomography measurements from exponential to linear scaling, enabling efficient characterization of fermionic quantum systems. Validated on SYK model and free-fermion chains across phase transitions.
#QuantumTomography #QuantumSimulation #News
#QuantumTomography #QuantumSimulation #News
Linear-Scaling Quantum State Tomography for Fermionic Many-Body Systems
quantumzeitgeist.com
July 31, 2026 at 11:23 AM
Protocol reduces tomography measurements from exponential to linear scaling, enabling efficient characterization of fermionic quantum systems. Validated on SYK model and free-fermion chains across phase transitions.
#QuantumTomography #QuantumSimulation #News
#QuantumTomography #QuantumSimulation #News
Extends phase retrieval theory from pure quantum states to mixed states of bounded rank, establishing stability characterizations for quantum superoperators and measurements with applications to quantum tomography.
#QuantumInformation #QuantumTomography #Research
#QuantumInformation #QuantumTomography #Research
Phase Retrievability of Superoperators and Measurements in Quantum Information Theory
arxiv.org
July 30, 2026 at 5:05 AM
Extends phase retrieval theory from pure quantum states to mixed states of bounded rank, establishing stability characterizations for quantum superoperators and measurements with applications to quantum tomography.
#QuantumInformation #QuantumTomography #Research
#QuantumInformation #QuantumTomography #Research
Develops quantum-tomographic methods to distinguish CP violation in top-quark pair production from decay-level effects using angular distributions as spin analyzers, providing systematic separation strategy for fundamental symmetry studies.
#TopQuarks #CPViolation #QuantumTomography
#TopQuarks #CPViolation #QuantumTomography
Quantum Tomography Framework for Separating CP Violation Sources in Top-Antitop Production and Decay
arxiv.org
July 29, 2026 at 8:19 AM
Develops quantum-tomographic methods to distinguish CP violation in top-quark pair production from decay-level effects using angular distributions as spin analyzers, providing systematic separation strategy for fundamental symmetry studies.
#TopQuarks #CPViolation #QuantumTomography
#TopQuarks #CPViolation #QuantumTomography
Developed scalable tomography protocol for fermionic quantum systems achieving linear measurement complexity instead of exponential. Reconstructs quantum states from particle-number and momentum distributions accessible in ultracold-atom experiments.
#QuantumTomography #QuantumSimulation #Research
#QuantumTomography #QuantumSimulation #Research
Permutationally Invariant Quantum State Tomography for Fermions
arxiv.org
July 28, 2026 at 2:22 AM
Developed scalable tomography protocol for fermionic quantum systems achieving linear measurement complexity instead of exponential. Reconstructs quantum states from particle-number and momentum distributions accessible in ultracold-atom experiments.
#QuantumTomography #QuantumSimulation #Research
#QuantumTomography #QuantumSimulation #Research
SSP-QST enables rank-adaptive reconstruction of photonic quantum states without priors, achieving 8× photon efficiency gain and highest fidelity across probe ranks via Weyl-theoretic spectral thresholding—ideal for real-time feedback sensing.
#PhotonicQuantum #QuantumSensing #QuantumTomography
#PhotonicQuantum #QuantumSensing #QuantumTomography
Spectral Subspace Purification for Photonic Quantum State Tomography
arxiv.org
July 27, 2026 at 7:46 AM
SSP-QST enables rank-adaptive reconstruction of photonic quantum states without priors, achieving 8× photon efficiency gain and highest fidelity across probe ranks via Weyl-theoretic spectral thresholding—ideal for real-time feedback sensing.
#PhotonicQuantum #QuantumSensing #QuantumTomography
#PhotonicQuantum #QuantumSensing #QuantumTomography
Developed a scalable quantum state tomography algorithm using rank-adaptive matrix-free density operator representation, achieving superior accuracy-runtime-memory tradeoffs without forming dense matrices.
#QuantumAlgorithms #QuantumTomography #Scalability
#QuantumAlgorithms #QuantumTomography #Scalability
Rank-Adaptive Matrix-Free Quantum State Tomography
arxiv.org
July 23, 2026 at 8:50 AM
Developed a scalable quantum state tomography algorithm using rank-adaptive matrix-free density operator representation, achieving superior accuracy-runtime-memory tradeoffs without forming dense matrices.
#QuantumAlgorithms #QuantumTomography #Scalability
#QuantumAlgorithms #QuantumTomography #Scalability
New framework reveals when quantum state reconstruction from finite measurements is statistically stable. Shows informational completeness is necessary but not sufficient—measurement frame bounds determine true reconstructibility.
#QuantumEstimation #QuantumTomography #Research
#QuantumEstimation #QuantumTomography #Research
Measurement Frames and σ-Regularized Estimation for Continuous-Variable Quantum Systems
arxiv.org
July 22, 2026 at 3:41 AM
New framework reveals when quantum state reconstruction from finite measurements is statistically stable. Shows informational completeness is necessary but not sufficient—measurement frame bounds determine true reconstructibility.
#QuantumEstimation #QuantumTomography #Research
#QuantumEstimation #QuantumTomography #Research
Proven: quantum memory provides provable query-complexity advantage in learning quantum channels. Coherent protocols achieve O(D²/ε²) scaling vs O(D³/ε²) for best classical adaptive methods, establishing strict quantum memory separation.
#QuantumMemory #QuantumTomography #Research
#QuantumMemory #QuantumTomography #Research
Quantum Memory Advantage for Quantum Process Tomography
iq.fp2.dev
July 16, 2026 at 3:55 AM
Proven: quantum memory provides provable query-complexity advantage in learning quantum channels. Coherent protocols achieve O(D²/ε²) scaling vs O(D³/ε²) for best classical adaptive methods, establishing strict quantum memory separation.
#QuantumMemory #QuantumTomography #Research
#QuantumMemory #QuantumTomography #Research
Resolved: the sample complexity of learning Gaussian quantum states. Both bosonic and fermionic m-mode systems require O(m²) copies, proving non-Gaussian operations fundamentally outperform Gaussian-only measurement strategies for tomography.
#QuantumTomography #QuantumLearning #Research
#QuantumTomography #QuantumLearning #Research
Optimal Tomography of Bosonic and Fermionic Gaussian States
arxiv.org
July 14, 2026 at 6:34 AM
Resolved: the sample complexity of learning Gaussian quantum states. Both bosonic and fermionic m-mode systems require O(m²) copies, proving non-Gaussian operations fundamentally outperform Gaussian-only measurement strategies for tomography.
#QuantumTomography #QuantumLearning #Research
#QuantumTomography #QuantumLearning #Research
Novel tomography approach enables efficient characterization of noisy quantum channels, outperforming existing methods while requiring fewer resources. Particularly valuable for NISQ device users interpreting computational results beyond vendor specifications.
#QuantumTomography #NISQ #Research
#QuantumTomography #NISQ #Research
Procrustes Tomography: Efficient Quantum Channel Characterization
arxiv.org
July 10, 2026 at 6:34 AM
Novel tomography approach enables efficient characterization of noisy quantum channels, outperforming existing methods while requiring fewer resources. Particularly valuable for NISQ device users interpreting computational results beyond vendor specifications.
#QuantumTomography #NISQ #Research
#QuantumTomography #NISQ #Research
Researchers achieved optimal O(dε⁻¹) query complexity for learning quantum evolutions using classical shadow estimation, reducing resource demands and establishing new benchmarks for quantum system characterization.
#QuantumLearning #QuantumTomography #News
#QuantumLearning #QuantumTomography #News
Query-Optimal Quantum Process Tomography via Classical Shadow Estimation
quantumzeitgeist.com
June 22, 2026 at 3:38 PM
Researchers achieved optimal O(dε⁻¹) query complexity for learning quantum evolutions using classical shadow estimation, reducing resource demands and establishing new benchmarks for quantum system characterization.
#QuantumLearning #QuantumTomography #News
#QuantumLearning #QuantumTomography #News
Proving quantum state tomography complexity: detecting large Pauli coefficients is NP-hard for quantum algorithms. Reduction from minimum-weight code problem shows efficient Pauli sampling algorithms cannot exist under standard complexity assumptions.
#QuantumComplexity #QuantumTomography #Research
#QuantumComplexity #QuantumTomography #Research
Complexity of Detecting Large Coefficients in the Pauli Basis
iq.fp2.dev
June 19, 2026 at 10:20 AM
Proving quantum state tomography complexity: detecting large Pauli coefficients is NP-hard for quantum algorithms. Reduction from minimum-weight code problem shows efficient Pauli sampling algorithms cannot exist under standard complexity assumptions.
#QuantumComplexity #QuantumTomography #Research
#QuantumComplexity #QuantumTomography #Research