01429nas a2200157 4500008004100000245005600041210005600097260001300153520097200166100001801138700002301156700001801179700002101197700001601218856003701234 2023 eng d00aRandom Pulse Sequences for Qubit Noise Spectroscopy0 aRandom Pulse Sequences for Qubit Noise Spectroscopy c3/2/20233 a
Qubit noise spectroscopy is an important tool for the experimental investigation of open quantum systems. However, conventional techniques for implementing noise spectroscopy are time-consuming, because they require multiple measurements of the noise spectral density at different frequencies. Here we describe an alternative method for quickly characterizing the spectral density. Our method utilizes random pulse sequences, with carefully-controlled correlations among the pulses, to measure arbitrary linear functionals of the noise spectrum. Such measurements allow us to estimate k'th-order moments of the noise spectrum, as well as to reconstruct sparse noise spectra via compressed sensing. Our simulations of the performance of the random pulse sequences on a realistic physical system, self-assembled quantum dots, reveal a speedup of an order of magnitude in extracting the noise spectrum compared to conventional dynamical decoupling approaches.
1 aHuang, Kaixin1 aFarfurnik, Demitry1 aSeif, Alireza1 aHafezi, Mohammad1 aLiu, Yi-Kai uhttps://arxiv.org/abs/2303.0090902086nas a2200133 4500008004100000245006600041210006500107260001400172520167400186100001801860700002101878700001601899856003701915 2021 eng d00aCompressed Sensing Measurement of Long-Range Correlated Noise0 aCompressed Sensing Measurement of LongRange Correlated Noise c5/26/20213 aLong-range correlated errors can severely impact the performance of NISQ (noisy intermediate-scale quantum) devices, and fault-tolerant quantum computation. Characterizing these errors is important for improving the performance of these devices, via calibration and error correction, and to ensure correct interpretation of the results. We propose a compressed sensing method for detecting two-qubit correlated dephasing errors, assuming only that the correlations are sparse (i.e., at most s pairs of qubits have correlated errors, where s << n(n-1)/2, and n is the total number of qubits). In particular, our method can detect long-range correlations between any two qubits in the system (i.e., the correlations are not restricted to be geometrically local).
Our method is highly scalable: it requires as few as m = O(s log n) measurement settings, and efficient classical postprocessing based on convex optimization. In addition, when m = O(s log^4(n)), our method is highly robust to noise, and has sample complexity O(max(n,s)^2 log^4(n)), which can be compared to conventional methods that have sample complexity O(n^3). Thus, our method is advantageous when the correlations are sufficiently sparse, that is, when s < O(n^(3/2) / log^2(n)). Our method also performs well in numerical simulations on small system sizes, and has some resistance to state-preparation-and-measurement (SPAM) errors. The key ingredient in our method is a new type of compressed sensing measurement, which works by preparing entangled Greenberger-Horne-Zeilinger states (GHZ states) on random subsets of qubits, and measuring their decay rates with high precision.
We use machine learning to classify rational two-dimensional conformal field theories. We first use the energy spectra of these minimal models to train a supervised learning algorithm. We find that the machine is able to correctly predict the nature and the value of critical points of several strongly correlated spin models using only their energy spectra. This is in contrast to previous works that use machine learning to classify different phases of matter, but do not reveal the nature of the critical point between phases. Given that the ground-state entanglement Hamiltonian of certain topological phases of matter is also described by conformal field theories, we use supervised learning on Réyni entropies and find that the machine is able to identify which conformal field theory describes the entanglement Hamiltonian with only the lowest few Réyni entropies to a high degree of accuracy. Finally, using autoencoders, an unsupervised learning algorithm, we find a hidden variable that has a direct correlation with the central charge and discuss prospects for using machine learning to investigate other conformal field theories, including higher-dimensional ones. Our results highlight that machine learning can be used to find and characterize critical points and also hint at the intriguing possibility to use machine learning to learn about more complex conformal field theories.
1 aKuo, En-Jui1 aSeif, Alireza1 aLundgren, Rex1 aWhitsitt, Seth1 aHafezi, Mohammad uhttps://arxiv.org/abs/2106.1348501944nas a2200181 4500008004100000245006800041210006700109260001400176520137900190100002201569700001801591700001801609700002401627700002801651700002101679700002501700856003701725 2021 eng d00aDiscovering hydrodynamic equations of many-body quantum systems0 aDiscovering hydrodynamic equations of manybody quantum systems c11/3/20213 aSimulating and predicting dynamics of quantum many-body systems is extremely challenging, even for state-of-the-art computational methods, due to the spread of entanglement across the system. However, in the long-wavelength limit, quantum systems often admit a simplified description, which involves a small set of physical observables and requires only a few parameters such as sound velocity or viscosity. Unveiling the relationship between these hydrodynamic equations and the underlying microscopic theory usually requires a great effort by condensed matter theorists. In the present paper, we develop a new machine-learning framework for automated discovery of effective equations from a limited set of available data, thus bypassing complicated analytical derivations. The data can be generated from numerical simulations or come from experimental quantum simulator platforms. Using integrable models, where direct comparisons can be made, we reproduce previously known hydrodynamic equations, strikingly discover novel equations and provide their derivation whenever possible. We discover new hydrodynamic equations describing dynamics of interacting systems, for which the derivation remains an outstanding challenge. Our approach provides a new interpretable method to study properties of quantum materials and quantum simulators in non-perturbative regimes.
1 aKharkov, Yaroslav1 aShtanko, Oles1 aSeif, Alireza1 aBienias, Przemyslaw1 aVan Regemortel, Mathias1 aHafezi, Mohammad1 aGorshkov, Alexey, V. uhttps://arxiv.org/abs/2111.0238501402nas a2200133 4500008004100000245003000041210003000071260001300101520105400114100002401168700001801192700002101210856003701231 2021 eng d00aMeta Hamiltonian Learning0 aMeta Hamiltonian Learning c4/9/20213 aEfficient characterization of quantum devices is a significant challenge critical for the development of large scale quantum computers. We consider an experimentally motivated situation, in which we have a decent estimate of the Hamiltonian, and its parameters need to be characterized and fine-tuned frequently to combat drifting experimental variables. We use a machine learning technique known as meta-learning to learn a more efficient optimizer for this task. We consider training with the nearest-neighbor Ising model and study the trained model's generalizability to other Hamiltonian models and larger system sizes. We observe that the meta-optimizer outperforms other optimization methods in average loss over test samples. This advantage follows from the meta-optimizer being less likely to get stuck in local minima, which highly skews the distribution of the final loss of the other optimizers. In general, meta-learning decreases the number of calls to the experiment and reduces the needed classical computational resources.
1 aBienias, Przemyslaw1 aSeif, Alireza1 aHafezi, Mohammad uhttps://arxiv.org/abs/2104.0445301656nas a2200157 4500008004100000245008100041210006900122260001500191520114900206100002801355700001701383700001801400700002201418700002101440856003701461 2020 eng d00aEntanglement entropy scaling transition under competing monitoring protocols0 aEntanglement entropy scaling transition under competing monitori c08/19/20203 aDissipation generally leads to the decoherence of a quantum state. In contrast, numerous recent proposals have illustrated that dissipation can also be tailored to stabilize many-body entangled quantum states. While the focus of these works has been primarily on engineering the non-equilibrium steady state, we investigate the build-up of entanglement in the quantum trajectories. Specifically, we analyze the competition between two different dissipation channels arising from two incompatible continuous monitoring protocols. The first protocol locks the phase of neighboring sites upon registering a quantum jump, thereby generating a long-range entanglement through the system, while the second one destroys the coherence via dephasing mechanism. By studying the unraveling of stochastic quantum trajectories associated with the continuous monitoring protocols, we present a transition for the scaling of the averaged trajectory entanglement entropies, from critical scaling to area-law behavior. Our work provides novel insights into the occurrence of a measurement-induced phase transition within a continuous monitoring protocol.
1 aVan Regemortel, Mathias1 aCian, Ze-Pei1 aSeif, Alireza1 aDehghani, Hossein1 aHafezi, Mohammad uhttps://arxiv.org/abs/2008.0861901316nas a2200145 4500008004100000245005300041210005300094260001500147300000800162520089700170100001801067700002101085700002701106856003701133 2020 eng d00aMachine learning the thermodynamic arrow of time0 aMachine learning the thermodynamic arrow of time c09/21/2020 a1-93 aThe mechanism by which thermodynamics sets the direction of time's arrow has long fascinated scientists. Here, we show that a machine learning algorithm can learn to discern the direction of time's arrow when provided with a system's microscopic trajectory as input. The performance of our algorithm matches fundamental bounds predicted by nonequilibrium statistical mechanics. Examination of the algorithm's decision-making process reveals that it discovers the underlying thermodynamic mechanism and the relevant physical observables. Our results indicate that machine learning techniques can be used to study systems out of equilibrium, and ultimately to uncover physical principles.
1 aSeif, Alireza1 aHafezi, Mohammad1 aJarzynski, Christopher uhttps://arxiv.org/abs/1909.1238001577nas a2200157 4500008004100000245004200041210004200083260001400125520114200139100001701281700002301298700001801321700002401339700001901363856003701382 2020 eng d00aOptimal control for quantum detectors0 aOptimal control for quantum detectors c5/12/20203 aQuantum systems are promising candidates for sensing of weak signals as they can provide unrivaled performance when estimating parameters of external fields. However, when trying to detect weak signals that are hidden by background noise, the signal-to-noise-ratio is a more relevant metric than raw sensitivity. We identify, under modest assumptions about the statistical properties of the signal and noise, the optimal quantum control to detect an external signal in the presence of background noise using a quantum sensor. Interestingly, for white background noise, the optimal solution is the simple and well-known spin-locking control scheme. We further generalize, using numerical techniques, these results to the background noise being a correlated Lorentzian spectrum. We show that for increasing correlation time, pulse based sequences such as CPMG are also close to the optimal control for detecting the signal, with the crossover dependent on the signal frequency. These results show that an optimal detection scheme can be easily implemented in near-term quantum sensors without the need for complicated pulse shaping.
1 aTitum, Paraj1 aSchultz, Kevin, M.1 aSeif, Alireza1 aQuiroz, Gregory, D.1 aClader, B., D. uhttps://arxiv.org/abs/2005.0599501870nas a2200181 4500008004100000245008300041210006900124260001300193490000600206520132100212100002001533700002101553700002401574700001801598700001801616700001701634856003701651 2020 eng d00aTowards analog quantum simulations of lattice gauge theories with trapped ions0 aTowards analog quantum simulations of lattice gauge theories wit c4/8/20200 v23 aGauge field theories play a central role in modern physics and are at the heart of the Standard Model of elementary particles and interactions. Despite significant progress in applying classical computational techniques to simulate gauge theories, it has remained a challenging task to compute the real-time dynamics of systems described by gauge theories. An exciting possibility that has been explored in recent years is the use of highly-controlled quantum systems to simulate, in an analog fashion, properties of a target system whose dynamics are difficult to compute. Engineered atom-laser interactions in a linear crystal of trapped ions offer a wide range of possibilities for quantum simulations of complex physical systems. Here, we devise practical proposals for analog simulation of simple lattice gauge theories whose dynamics can be mapped onto spin-spin interactions in any dimension. These include 1+1D quantum electrodynamics, 2+1D Abelian Chern-Simons theory coupled to fermions, and 2+1D pure Z2 gauge theory. The scheme proposed, along with the optimization protocol applied, will have applications beyond the examples presented in this work, and will enable scalable analog quantum simulation of Heisenberg spin models in any number of dimensions and with arbitrary interaction strengths.
1 aDavoudi, Zohreh1 aHafezi, Mohammad1 aMonroe, Christopher1 aPagano, Guido1 aSeif, Alireza1 aShaw, Andrew uhttps://arxiv.org/abs/1908.0321001434nas a2200193 4500008004100000245005500041210005500096260001500151490000800166520090200174100001701076700001601093700001701109700001801126700002101144700001701165700002101182856003701203 2019 eng d00aPhoton pair condensation by engineered dissipation0 aPhoton pair condensation by engineered dissipation c04/02/20190 v1233 aDissipation can usually induce detrimental decoherence in a quantum system. However, engineered dissipation can be used to prepare and stabilize coherent quantum many-body states. Here, we show that by engineering dissipators containing photon pair operators, one can stabilize an exotic dark state, which is a condensate of photon pairs with a phase-nematic order. In this system, the usual superfluid order parameter, i.e. single-photon correlation, is absent, while the photon pair correlation exhibits long-range order. Although the dark state is not unique due to multiple parity sectors, we devise an additional type of dissipators to stabilize the dark state in a particular parity sector via a diffusive annihilation process which obeys Glauber dynamics in an Ising model. Furthermore, we propose an implementation of these photon-pair dissipators in circuit-QED architecture.
1 aCian, Ze-Pei1 aZhu, Guanyu1 aChu, Su-Kuan1 aSeif, Alireza1 aDeGottardi, Wade1 aJiang, Liang1 aHafezi, Mohammad uhttps://arxiv.org/abs/1904.0001600667nas a2200145 4500008004100000245004500041210004400086260001400130300001000144490000700154520024100161100001800402700002100420856008000441 2018 eng d00aBroadband optomechanical non-reciprocity0 aBroadband optomechanical nonreciprocity c2018/1/26 a60-610 v123 aImplementing non-reciprocal elements with a bandwidth comparable to optical frequencies is a challenge
in integrated photonics. Now, a phonon pump has been used to achieve optical non-reciprocity over a
large bandwidth.
We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach allows the incorporation of any number of features of the data with minimal modifications to the underlying network architecture. We experimentally illustrate this approach in the readout of trapped-ion qubits using additional spatial and temporal features in the data. Using this neural network classifier, we efficiently treat qubit readout crosstalk, resulting in a 30\% improvement in detection error over the conventional threshold method. Our approach does not depend on the specific details of the system and can be readily generalized to other quantum computing platforms.
1 aSeif, Alireza1 aLandsman, Kevin, A.1 aLinke, Norbert, M.1 aFiggatt, Caroline1 aMonroe, C.1 aHafezi, Mohammad uhttps://arxiv.org/abs/1804.0771801505nas a2200157 4500008004100000245008300041210006900124260001400193490000900207520101200216100001801228700002101246700002201267700002101289856003701310 2018 eng d00aThermal management and non-reciprocal control of phonon flow via optomechanics0 aThermal management and nonreciprocal control of phonon flow via c2018/3/230 v9(1)3 aEngineering phonon transport in physical systems is a subject of interest in the study of materials and plays a crucial role in controlling energy and heat transfer. Of particular interest are non-reciprocal phononic systems, which in direct analogy to electric diodes, provide a directional flow of energy. Here, we propose an engineered nanostructured material, in which tunable non-reciprocal phonon transport is achieved through optomechanical coupling. Our scheme relies on breaking time-reversal symmetry by a spatially varying laser drive, which manipulates low-energy acoustic phonons. Furthermore, we take advantage of recent developments in the manipulation of high-energy phonons through controlled scattering mechanisms, such as using alloys and introducing disorder. These combined approaches allow us to design an acoustic isolator and a thermal diode. Our proposed device will have potential impact in phonon-based information processing, and heat management in low temperatures.
1 aSeif, Alireza1 aDeGottardi, Wade1 aEsfarjani, Keivan1 aHafezi, Mohammad uhttps://arxiv.org/abs/1710.0896701586nas a2200169 4500008004100000245006900041210006900110260001500179490000900194520108300203100002001286700001601306700001801322700001801340700002101358856003701379 2016 eng d00aMeasurement Protocol for the Entanglement Spectrum of Cold Atoms0 aMeasurement Protocol for the Entanglement Spectrum of Cold Atoms c2016/11/220 v6(4)3 aEntanglement, and, in particular the entanglement spectrum, plays a major role in characterizing many-body quantum systems. While there has been a surge of theoretical works on the subject, no experimental measurement has been performed to date because of the lack of an implementable measurement scheme. Here, we propose a measurement protocol to access the entanglement spectrum of many-body states in experiments with cold atoms in optical lattices. Our scheme effectively performs a Ramsey spectroscopy of the entanglement Hamiltonian and is based on the ability to produce several copies of the state under investigation together with the possibility to perform a global swap gate between two copies conditioned on the state of an auxiliary qubit. We show how the required conditional swap gate can be implemented with cold atoms, either by using Rydberg interactions or coupling the atoms to a cavity mode. We illustrate these ideas on a simple (extended) Bose-Hubbard model where such a measurement protocol reveals topological features of the Haldane phase.
1 aPichler, Hannes1 aZhu, Guanyu1 aSeif, Alireza1 aZoller, Peter1 aHafezi, Mohammad uhttps://arxiv.org/abs/1605.08624