[en] A quantum machine that accepts an input and processes it in parallel is described. The logic variables of the machine are not wavefunctions (qubits) but observables (i.e., operators) and its operation is described in the Heisenberg picture. The active core is a solid-state assembly of small nanosized colloidal quantum dots (QDs) or dimers of dots. The size dispersion of the QDs that causes fluctuations in their discrete electronic energies is a limiting factor. The input to the machine is provided by a train of very brief laser pulses, at least four in number. The coherent band width of each ultrashort pulse needs to span at least several and preferably all the single electron excited states of the dots. The spectrum of the QD assembly is measured as a function of the time delays between the input laser pulses. The dependence of the spectrum on the time delays can be Fourier transformed to a frequency spectrum. This spectrum of a finite range in time is made up of discrete pixels. These are the visible, raw, basic logic variables. The spectrum is analyzed to determine a possibly smaller number of principal components. A Lie-algebraic point of view is used to explore the use of the machine to emulate the dynamics of other quantum systems. An explicit example demonstrates the considerable quantum advantage of our scheme.
Disciplines :
Chemistry
Author, co-author :
Remacle, Françoise ; Université de Liège - ULiège > Département de chimie (sciences) > Laboratoire de chimie physique théorique
Levine, Raphael D.; Hebrew University of Jerusalem
Language :
English
Title :
A Quantum Information Processing Machine for Computing by Observables
Publication date :
2023
Journal title :
Proceedings of the National Academy of Sciences of the United States of America
ISSN :
0027-8424
eISSN :
1091-6490
Publisher :
National Academy of Sciences, Washington, United States - District of Columbia
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
J. Yuen-Zhou, J. J. Krich, I. Kassal, A. S. Johnson, A. Aspuru-Guzik, "Ultrafast spectroscopy" in Quantum Information and Wavepackets (IOP Publishing, Bristol, 2014).
E. Collini, 2D electronic spectroscopic techniques for quantum technology applications. J. Phys. Chem. C 125, 13096-13108 (2021).
S. Mukamel, "Principles of Nonlinear Optical Spectroscopy" in Oxford Series in Optical and Imaging Sciences (Oxford University Press, 1995).
P. Brumer, M. Shapiro, Coherence chemistry: Controlling chemical reactions [with lasers]. Acc. Chem. Res. 22, 407-413 (1989).
J. D. Hybl, A. W. Albrecht, S. M. Gallagher Faeder, D. M. Jonas, Two-dimensional electronic spectroscopy. Chem. Phys. Lett. 297, 307-313 (1998).
M. D. Fayer, Dynamics of liquids, molecules, and proteins measured with ultrafast 2D IR vibrational echo chemical exchange spectroscopy. Ann. Rev. Phys. Chem. 60, 21-38 (2009).
P. Hamm, M. T. Zanni, Concepts and Methods of 2D Infrared Spectroscopy (Cambridge University Press, Cambridge, 2011).
A. Gelzinis, R. Augulis, V. Butkus, B. Robert, L. Valkunas, Two-dimensional spectroscopy for non-specialists. Biochim. et Biophys. Acta (BBA) Bioenerg. 1860, 271-285 (2019).
F. D. Fuller, J. P. Ogilvie, Experimental implementations of two-dimensional fourier transform electronic spectroscopy. Ann. Rev. Phys. Chem. 66, 667-690 (2015).
A. M. Brańczyk, D. B. Turner, G. D. Scholes, Crossing disciplines-A view on two-dimensional optical spectroscopy. Annalen der Physik 526, 31-49 (2014).
G. D. Scholes et al., Using coherence to enhance function in chemical and biophysical systems. Nat. 543, 647-656 (2017).
Y. Kim et al., Quantum biology: An update and perspective. Quantum Rep. 3, 80-126 (2021).
E. Collini, H. Gattuso, R. D. Levine, F. Remacle, Ultrafast Fs coherent excitonic dynamics in CdSe quantum dots assemblies addressed and probed by 2D electronic spectroscopy. J. Chem. Phys. 154, 014301 (2021).
E. Collini et al., Room-temperature inter-dot coherent dynamics in multilayer quantum dot materials. J. Phys. Chem. C. 124, 16222-16231 (2020).
J. R. Hamilton et al., Harvesting a wide spectral range of electronic coherences with disordered quasi-homo dimeric assemblies at room temperature. Adv. Quantum Technol. 5, 2200060 (2022).
D. B. Turner, Y. Hassan, G. D. Scholes, Exciton superposition states in CdSe nanocrystals measured using broadband two-dimensional electronic spectroscopy. Nano Lett. 12, 880-886 (2012).
E. Cassette, J. C. Dean, G. D. Scholes, Two-dimensional visible spectroscopy for studying colloidal semiconductor nanocrystals. Small 12, 2234-2244 (2016).
N. Lenngren et al., Hot electron and hole dynamics in thiol capped CdSe quantum dots revealed by 2D electronic spectroscopy. Phys. Chem. Chem. Phys. 18, 26199-26204 (2016).
S. Palato, H. Seiler, P. Nijjar, O. Prezhdo, P. Kambhampati, Atomic fluctuations in electronic materials revealed by dephasing. Proc. Natl. Acad. Sci. USA 117, 11940-11946 (2020).
Z. Wang et al., Excited states and their dynamics in CdSe quantum dots studied by two-color. J. Phys. Chem. Lett 13, 1266-1271 (2022).
J. R. Caram et al., Persistent interexcitonic quantum coherence in CdSe quantum dots. J. Phys. Chem. Lett. 5, 196-204 (2014).
H. Gattuso, R. D. Levine, F. Remacle, Massively parallel classical logic via coherent dynamics of an ensemble of quantum systems with dispersion in size. Proc. Natl. Acad. Sci. U.S.A. 117, 21022 (2020).
M. A. Nielsen, I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, 2010).
G. Carleo et al., Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019).
J. Biamonte et al., Quantum machine learning. Nature 549, 195-202 (2017).
M. Sajjan et al., Quantum machine learning for chemistry and physics. Chem. Soc. Rev. 51, 6475-6573 (2022).
Y. Chi et al., A programmable qudit-based quantum processor. Nat. Commun. 13, 1166 (2022).
C. Reimer et al., High-dimensional one-way quantum processing implemented on d-level cluster states. Nat. Phys. 15, 148-153 (2019).
H.-H. Lu et al., Quantum phase estimation with time-frequency qudits in a single photon. Adv. Quantum Technol. 3, 1900074 (2020).
J. A. Gyamfi, Fundamentals of quantum mechanics in Liouville space. Eur. J. Phys. 41, 063002 (2020).
E. J. Heller, Time-dependent approach to semiclassical dynamics. J. Chem. Phys. 62, 1544-1555 (1975).
D. J. Tannor, Introduction to Quantum Mechanics. A Time-Dependent Perspective (University Science Book, Sausalito, 2007).
E. Collini et al., Quantum phenomena in nanomaterials: Coherent superpositions of fine structure states in CdSe nanocrystals at room temperature. J. Phys. Chem. C 123, 31286-31293 (2019).
S. Mueller et al., Fluorescence-detected two-quantum and one-quantum-two-quantum 2D electronic spectroscopy. J. Phys. Chem. Lett. 9, 1964-1969 (2018).
L. Bolzonello et al., Photocurrent-detected 2D electronic spectroscopy reveals ultrafast hole transfer in operating PM6/Y6 organic solar cells. J. Phys. Chem. Lett. 12, 3983-3988 (2021).
K. J. Karki et al., Coherent two-dimensional photocurrent spectroscopy in a PbS quantum dot photocell. Nat. Commun. 5, 5869 (2014).
A. A. Bakulin, C. Silva, E. Vella, Ultrafast spectroscopy with photocurrent detection: Watching excitonic optoelectronic systems at work. J. Phys. Chem. Lett. 7, 250-258 (2016).
R. P. Feynman, Simulating physics with computers. Int. J. Theor. Phys. 21, 467-488 (1982).
J. Yuen-Zhou, J. J. Krich, M. Mohseni, A. Aspuru-Guzik, Quantum state and process tomography of energy transfer systems via ultrafast spectroscopy. Proc. Natl. Acad. Sci. U.S.A. 108, 17615-17620 (2011).
B. Fresch, D. Hiluf, E. Collini, R. D. Levine, F. Remacle, Molecular decision trees realized by ultrafast electronic spectroscopy. Proc. Natl. Acad. Sci. U.S.A. 110, 17183-17188 (2013).
K. Komarova, H. Gattuso, R. D. Levine, F. Remacle, Quantum device emulates the dynamics of two coupled oscillators. J. Phys. Chem. Lett. 11, 6990-6995 (2020).
K. Komarova, H. Gattuso, R. D. Levine, F. Remacle, Parallel quantum computation of vibrational dynamics. Front. Phys. 8, 486 (2020).
H. Li, A. D. Bristow, M. E. Siemens, G. Moody, S. T. Cundiff, Unraveling quantum pathways using optical 3D Fourier-transform spectroscopy. Nat. Commun. 4, 1390-1390 (2013).
J. C. Light, T. Carrington Jr., "Discrete-variable representations and their utilization" in Advances in Chemical Physics (John Wiley & Sons, Inc., 2000), pp. 263-310, https://doi. org/10.1002/9780470141731.ch4.
G. H. Golub, V. Pereyra, The differentiation of pseudo-inverses and nonlinear least squares problems whose variables separate. SIAM J. Num. Anal. 10, 413-432 (1973).
F. Remacle, S. A. Goldstein, D. R. Levine, Multivariate surprisal analysis of gene expression levels. Entropy 18, 445 (2017).
G. H. Golub, C. F. V. Loan, Matrix Computations (John Hopkins University Press, Baltimore, 2013).
G. H. Golub, C. Reinsch, "Singular value decomposition and least squares solutions" in Linear Algebra, J. H. Wilkinson, C. Reinsch, F. L. Bauer, Eds. (Springer, Berlin, Heidelberg, 1971), pp. 134-151, 10.1007/978-3-662-39778-7_ 10.
F. Remacle, N. Kravchenko-Balasha, A. Levitzki, R. D. Levine, Information-theoretic analysis of phenotype changes in early stages of carcinogenesis. Proc. Natl. Acad. Sci. U.S.A. 107, 10324-10329 (2010).
R. W. Hendler, R. I. Shrager, Deconvolutions based on singular value decomposition and the pseudoinverse: A guide for beginners. J. Biochem. Biophys. Methods 28, 1-33 (1994).
J. Wei, E. Norman, Lie algebraic solution of linear differential equations. J. Math. Phys. 4, 575-581 (1963).
J. Wei, E. Norman, On global representations of the solutions of linear differential equations as a product of exponentials. Proc. Am. Math. So. 15, 327-334 (1964).
Y. Alhassid, R. D. Levine, Connection between the maximal entropy and the scattering theoretic analyses of collision processes. Phys. Rev. A 18, 89-116 (1978).
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
