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===Reservoir computing===
{{main|Reservoir computing}}
Reservoir computing is a computational framework derived from recurrent neural network theory that involves mapping input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. The reservoir, which can be virtual or physical, is made up of individual non-linear units that are connected in recurrent loops, allowing it to store information. Training is performed only at the readout stage, as the reservoir dynamics are fixed, and this framework allows for the use of naturally available systems, both classical and quantum mechanical, to reduce the effective computational cost. One key benefit of reservoir computing is that it allows for a simple and fast learning algorithm, as well as hardware implementation through [[Reservoir computing#Physical reservoir computers|physical reservoirs]].<ref>{{Cite journal|last1=Tanaka|first1=Gouhei|last2=Yamane|first2=Toshiyuki|last3=Héroux|first3=Jean Benoit|last4=Nakane|first4=Ryosho|last5=Kanazawa|first5=Naoki|last6=Takeda|first6=Seiji|last7=Numata|first7=Hidetoshi|last8=Nakano|first8=Daiju|last9=Hirose|first9=Akira|date=2019-07-01|title=Recent advances in physical reservoir computing: A review|journal=Neural Networks|language=en|volume=115|pages=100–123|doi=10.1016/j.neunet.2019.03.005|pmid=30981085 |issn=0893-6080|doi-access=free|arxiv=1808.04962}}</ref><ref>{{Cite journal|last1=Röhm|first1=André|last2=Lüdge|first2=Kathy|date=2018-08-03|title=Multiplexed networks: reservoir computing with virtual and real nodes|journal=Journal of Physics Communications|volume=2|issue=8|pages=085007|bibcode=2018JPhCo...2h5007R|doi=10.1088/2399-6528/aad56d|arxiv=1802.08590 |issn=2399-6528|doi-access=free}}</ref> <br />
===Tangible computing===
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===Spintronics===
{{main|Spintronics}}
Spintronics is a field of study that involves the use of the intrinsic spin and magnetic moment of electrons in solid-state devices.<ref>{{Cite journal | last1 = Wolf | first1 = S. A. | last2 = Chtchelkanova | first2 = A. Y. | last3 = Treger | first3 = D. M. | title = Spintronics—A retrospective and perspective | doi = 10.1147/rd.501.0101 | journal = IBM Journal of Research and Development | volume = 50 | pages = 101–110 | year = 2006 }}</ref><ref>{{Cite web|url=http://video.google.com/videoplay?docid=2927943907685656536&q=LevyResearch&ei=dxd1SNCtOqj2rAKxzf1p|title=Physics Profile: "Stu Wolf: True D! Hollywood Story"}}</ref><ref>[https://www.science.org/doi/abs/10.1126/science.1065389 Spintronics: A Spin-Based Electronics Vision for the Future]. Sciencemag.org (16 November 2001). Retrieved on 21 October 2013.</ref> It differs from traditional electronics in that it exploits the spin of electrons as an additional degree of freedom, which has potential applications in data storage and transfer,<ref name="Bhatti et al.">{{cite journal |first1=S. |last1=Bhatti |display-authors=etal |title=Spintronics based random access memory: a review |journal=Materials Today |year=2017 |volume=20 |issue=9 |pages=530–548 |doi=10.1016/j.mattod.2017.07.007|doi-access=free |hdl=10356/146755 |hdl-access=free }}</ref> as well as quantum and neuromorphic computing. Spintronic systems are often created using dilute magnetic semiconductors and Heusler alloys.
===Atomtronics===
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